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State-of-the-art solutions adopt Deep Learning (DL) models to improve TP accuracy for various multimedia systems. This article illustrates that designing black-box TP engines that depend solely on the model\u2019s capacity and power of learning does not achieve consistent accuracy across all throughput ranges. Additionally, we propose MATURE, a novel multistage DL-based TP model designed to capture network operating context to improve prediction accuracy. MATURE\u2019s prediction involves characterizing the operating context before estimating the network throughput. We show that MATURE delivers consistent, accurate prediction for all throughput ranges in both 4G and 5G networks. We also show that light-weight MATURE models that use quantized parameters maintain their accuracy while featuring up to 100\u00d7 faster inference, thus making them suitable for mobile implementation. Our real video streaming experiments further show that MATURE improves the average user Quality of Experience by up to 20% when compared to other TP methods.<\/jats:p>","DOI":"10.1145\/3724400","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T13:22:17Z","timestamp":1745587337000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["M\n                    <sup>2<\/sup>\n                    ATURE: Mobile Multistage Throughput Prediction for Adaptive Video Streaming in Cellular Networks"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5317-2143","authenticated-orcid":false,"given":"Darijo","family":"Raca","sequence":"first","affiliation":[{"name":"University of Sarajevo, Sarajevo, Bosnia and Herzegovina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3678-046X","authenticated-orcid":false,"given":"Gregory","family":"Provan","sequence":"additional","affiliation":[{"name":"University College Cork, Cork, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3405-0324","authenticated-orcid":false,"given":"Ahmed","family":"Zahran","sequence":"additional","affiliation":[{"name":"Computer Science, University College Cork, Cork, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"issue":"1","key":"e_1_3_2_2_2","first-page":"9","article-title":"A survey in techniques for imbalanced intrusion detection system datasets","volume":"17","author":"Abedzadeh Najmeh","year":"2023","unstructured":"Najmeh Abedzadeh and Matthew Jacobs. 2023. 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